This repository contains a method to detect Iranian vehicle license plates as a representation of vehicle presence in an image. We have utilized You Only Look Once version 3 (YOLO v.3) to detect the plates inside an input image. The method has the advantages of high accuracy and real-time performance, thanks to YOLO v.3 architecture. The presented system receives a series of vehicle images and produces the processed image with added bounding-boxes containing the vehicles' license plates. The flow of how we have trained and tested the application is published in a paper accessible from the citation section.
- Python v.3
- You Only Look Once (YOLO) v.3
- A vehicle image dataset containing 3000+ samples
You can download the weights file from this link. It can also be downloaded from the weights folder (splitted files).
Test on a single image:
python object_detection_yolo.py --image=bird.jpg
Test on a single video file:
python object_detection_yolo.py --video=cars.mp4
Test on the webcam:
python object_detection_yolo.py
Please cite the following paper in case you have used this repo:
@inproceedings{Khazaee2020,
author = {Khazaee, Saeed and Tourani, Ali and Soroori, Sajjad and Shahbahrami, Asadollah and Suen, Ching Y.},
title = {{A Real-Time License Plate Detection Method Using a Deep Learning Approach}},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-59830-3_37},
isbn = {9783030598297},
issn = {16113349},
keywords = {Automatic number-plate detection,Deep learning,Image processing,Intelligent Transportation Systems},
pages = {425--438},
volume = {12068 LNCS},
year = {2020}
}